






import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch

def     snake_case   (UpperCAmelCase__    )			->							Dict:
  UpperCamelCase_:   Optional[Any]        =							os.path.join(args.tf_model_dir      ,       'parameters.json'    )
  UpperCamelCase_:   int        =							json.loads(open(UpperCAmelCase__    ).read()    )
  if not params:
    raise ValueError(
        F'''It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.'''    )
  if not args.output.endswith('.pt'    ):
    UpperCamelCase_:   Union[str, Any]        =							args.output + '.pt'
  UpperCamelCase_:   Optional[Any]        =							OrderedDict()
  with tf.device('/CPU:0'    ):
    UpperCamelCase_:   Optional[int]        =							tf.train.load_checkpoint(args.tf_model_dir    )
    UpperCamelCase_:   str        =							reader.get_variable_to_shape_map()
    for key_name in shapes.keys():
      UpperCamelCase_:   Tuple        =							reader.get_tensor(UpperCAmelCase__    ).astype(np.floataa    )
      if key_name.endswith('/adam_m'    ) or key_name.endswith('/adam_v'    ):
        continue
      if key_name.startswith('pasts/'    ):
        if key_name.startswith('pasts/mlp'    ):
          UpperCamelCase_:   Tuple        =							int(key_name[9]    )
        elif key_name.startswith('pasts/out'    ):
          UpperCamelCase_:   Any        =							8
        UpperCamelCase_:   int        =							'model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
        UpperCamelCase_:   Optional[int]        =							vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
        UpperCamelCase_:   List[str]        =							torch.tensor(UpperCAmelCase__    )
      elif key_name.startswith('model/moe'    ):
        UpperCamelCase_:   Dict        =							int(key_name[9:].split('/'    )[0]    )
        if key_name.endswith('/switch_gating/kernel'    ):
          UpperCamelCase_:   str        =							'model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
          UpperCamelCase_:   List[str]        =							vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
          UpperCamelCase_:   Tuple        =							torch.tensor(UpperCAmelCase__    )
        elif key_name.endswith('/softmlp/kernel'    ):
          UpperCamelCase_:   Optional[int]        =							'model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
          UpperCamelCase_:   int        =							vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
          UpperCamelCase_:   Optional[Any]        =							torch.tensor(UpperCAmelCase__    )
        elif key_name.endswith('/wo/kernel'    ) or key_name.endswith('/wi/kernel'    ):
          UpperCamelCase_:   Tuple        =							key_name[-9:-7]
          for i in range(1_6    ):
            UpperCamelCase_:   str        =							'model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
            UpperCamelCase_:   Dict        =							(
                vnp[i].transpose([1, 0]    ).copy()
            )  # In Mesh-Tensorflow, it is one array, so it is divided
            UpperCamelCase_:   Any        =							torch.tensor(UpperCAmelCase__    )
      elif key_name.startswith('model/mlp'    ):
        UpperCamelCase_:   Tuple        =							int(key_name[9:].split('/'    )[0]    )
        if key_name.endswith('/p1/kernel'    ):
          UpperCamelCase_:   List[Any]        =							'model.blocks.%d.feed_forward.mlp.wi.weight' % player
          UpperCamelCase_:   Tuple        =							vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
          UpperCamelCase_:   Tuple        =							torch.tensor(UpperCAmelCase__    )
        elif key_name.endswith('/p1/bias'    ):
          UpperCamelCase_:   List[str]        =							'model.blocks.%d.feed_forward.mlp.wi.bias' % player
          UpperCamelCase_:   List[str]        =							vnp.copy()  # same because it is one dimensional
          UpperCamelCase_:   Optional[Any]        =							torch.tensor(UpperCAmelCase__    )
        elif key_name.endswith('/p2/kernel'    ):
          UpperCamelCase_:   Optional[Any]        =							'model.blocks.%d.feed_forward.mlp.wo.weight' % player
          UpperCamelCase_:   List[str]        =							vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
          UpperCamelCase_:   Optional[Any]        =							torch.tensor(UpperCAmelCase__    )
        elif key_name.endswith('/p2/bias'    ):
          UpperCamelCase_:   List[Any]        =							'model.blocks.%d.feed_forward.mlp.wo.bias' % player
          UpperCamelCase_:   Dict        =							vnp.copy()  # same because it is one dimensional
          UpperCamelCase_:   str        =							torch.tensor(UpperCAmelCase__    )
      elif key_name.startswith('model/ln'    ):
        UpperCamelCase_:   Optional[Any]        =							int(key_name[8:].split('/'    )[0]    )
        if key_name.endswith('/b'    ):
          UpperCamelCase_:   Optional[Any]        =							'model.blocks.%d.feed_forward.norm.bias' % player
          UpperCamelCase_:   Optional[Any]        =							vnp.copy()  # same because it is one dimensional
          UpperCamelCase_:   List[str]        =							torch.tensor(UpperCAmelCase__    )
        elif key_name.endswith('/g'    ):
          UpperCamelCase_:   Optional[Any]        =							'model.blocks.%d.feed_forward.norm.weight' % player
          UpperCamelCase_:   Tuple        =							vnp.copy()  # same because it is one dimensional
          UpperCamelCase_:   str        =							torch.tensor(UpperCAmelCase__    )
      elif key_name.startswith('model/att'    ):
        UpperCamelCase_:   Dict        =							int(key_name[9:].split('/'    )[0]    )
        if key_name.endswith('/qkv/kernel'    ):
          UpperCamelCase_:   Any        =							vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
          UpperCamelCase_:   List[Any]        =							state[:, 0, :, :]
          UpperCamelCase_:   Any        =							state[:, 1, :, :]
          UpperCamelCase_:   Optional[int]        =							state[:, 2, :, :]
          UpperCamelCase_:   Optional[Any]        =							(
              state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]    )
              .transpose([1, 0]    )
              .copy()
          )  # Mesh-Tensorflow is a diagonal matrix
          UpperCamelCase_:   Dict        =							(
              state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]    )
              .transpose([1, 0]    )
              .copy()
          )  # Mesh-Tensorflow is a diagonal matrix
          UpperCamelCase_:   Tuple        =							(
              state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]    )
              .transpose([1, 0]    )
              .copy()
          )  # Mesh-Tensorflow is a diagonal matrix
          UpperCamelCase_:   Optional[int]        =							'model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
          UpperCamelCase_:   List[Any]        =							torch.tensor(UpperCAmelCase__    )
          UpperCamelCase_:   str        =							'model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
          UpperCamelCase_:   Any        =							torch.tensor(UpperCAmelCase__    )
          UpperCamelCase_:   Union[str, Any]        =							'model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
          UpperCamelCase_:   List[Any]        =							torch.tensor(UpperCAmelCase__    )
        elif key_name.endswith('/o/kernel'    ):
          UpperCamelCase_:   Optional[int]        =							'model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
          UpperCamelCase_:   Optional[Any]        =							(
              vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]    ).transpose([1, 0]    ).copy()
          )  # Mesh-Tensorflow is a diagonal matrix
          UpperCamelCase_:   Union[str, Any]        =							torch.tensor(UpperCAmelCase__    )
      elif key_name.startswith('model/an'    ):
        UpperCamelCase_:   Union[str, Any]        =							int(key_name[8:].split('/'    )[0]    )
        if key_name.endswith('/b'    ):
          UpperCamelCase_:   Optional[int]        =							'model.blocks.%d.self_attn.norm.bias' % player
          UpperCamelCase_:   int        =							vnp.copy()  # same because it is one dimensional
          UpperCamelCase_:   List[str]        =							torch.tensor(UpperCAmelCase__    )
        elif key_name.endswith('/g'    ):
          UpperCamelCase_:   str        =							'model.blocks.%d.self_attn.norm.weight' % player
          UpperCamelCase_:   Optional[Any]        =							vnp.copy()  # same because it is one dimensional
          UpperCamelCase_:   List[str]        =							torch.tensor(UpperCAmelCase__    )
      elif (
          key_name.startswith('model/wte'    )
          or key_name.startswith('model/wpe'    )
          or key_name.startswith('model/ete'    )
      ):
        UpperCamelCase_:   int        =							{'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
            key_name[-3:]
        ]
        UpperCamelCase_:   int        =							'model.%s.weight' % nlayer
        UpperCamelCase_:   Dict        =							vnp.copy()  # same in embedded
        UpperCamelCase_:   Dict        =							torch.tensor(UpperCAmelCase__    )
        if key_name.startswith('model/wte'    ):
          UpperCamelCase_:   Dict        =							'lm_head.weight'
          UpperCamelCase_:   List[str]        =							vnp.copy()  # same in embedded
          UpperCamelCase_:   int        =							torch.tensor(UpperCAmelCase__    )
      elif key_name.startswith('model/wob'    ):
        UpperCamelCase_:   List[Any]        =							'final_logits_bias'
        UpperCamelCase_:   str        =							vnp.copy()  # same in embedded
        UpperCamelCase_:   Union[str, Any]        =							state.reshape((1, -1)    )
        UpperCamelCase_:   Optional[Any]        =							torch.tensor(UpperCAmelCase__    )
      elif key_name == "model/dense/kernel":
        UpperCamelCase_:   Any        =							'model.last_project.weight'
        UpperCamelCase_:   Dict        =							vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
        UpperCamelCase_:   str        =							torch.tensor(UpperCAmelCase__    )
      elif key_name == "model/dense_1/bias":
        UpperCamelCase_:   List[Any]        =							'model.last_project.bias'
        UpperCamelCase_:   Any        =							vnp.copy()  # same because it is one dimensional
        UpperCamelCase_:   str        =							torch.tensor(UpperCAmelCase__    )
  torch.save(UpperCAmelCase__      ,       args.output    )


if __name__ == "__main__":
   A_						:						Tuple						      =					argparse.ArgumentParser(
       description='model converter.', formatter_class=argparse.ArgumentDefaultsHelpFormatter
   )
   parser.add_argument('--tf_model_dir', metavar='PATH', type=str, required=True, help='import model')
   parser.add_argument('--output', metavar='PATH', type=str, required=True, help='output model')
   A_						:						int						      =					parser.parse_args()
   convert_tf_gptsan_to_pt(args)
"""simple docstring"""



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__:Tuple									=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Optional[int]									=			{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class  snake_case__      (       snake_case_       ):
 _snake_case       :					Union[str, Any]							  =						"""sew-d"""


 def __init__( self    ,		lowerCamelCase=32    ,		lowerCamelCase=768    ,		lowerCamelCase=12    ,		lowerCamelCase=12    ,		lowerCamelCase=3072    ,		lowerCamelCase=2    ,		lowerCamelCase=512    ,		lowerCamelCase=256    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=("p2c", "c2p")    ,		lowerCamelCase="layer_norm"    ,		lowerCamelCase="gelu_python"    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.0    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.02    ,		lowerCamelCase=1E-7    ,		lowerCamelCase=1E-5    ,		lowerCamelCase="group"    ,		lowerCamelCase="gelu"    ,		lowerCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)    ,		lowerCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)    ,		lowerCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)    ,		lowerCamelCase=False    ,		lowerCamelCase=128    ,		lowerCamelCase=16    ,		lowerCamelCase=True    ,		lowerCamelCase=0.05    ,		lowerCamelCase=10    ,		lowerCamelCase=2    ,		lowerCamelCase=0.0    ,		lowerCamelCase=10    ,		lowerCamelCase=0    ,		lowerCamelCase="mean"    ,		lowerCamelCase=False    ,		lowerCamelCase=False    ,		lowerCamelCase=256    ,		lowerCamelCase=0    ,		lowerCamelCase=1    ,		lowerCamelCase=2    ,		**lowerCamelCase    ,		):
        super().__init__(**lowerCamelCase    ,		pad_token_id=lowerCamelCase    ,		bos_token_id=lowerCamelCase    ,		eos_token_id=lowerCamelCase						)
        __a       =    hidden_size
        __a       =    feat_extract_norm
        __a       =    feat_extract_activation
        __a       =    list(lowerCamelCase						)
        __a       =    list(lowerCamelCase						)
        __a       =    list(lowerCamelCase						)
        __a       =    conv_bias
        __a       =    num_conv_pos_embeddings
        __a       =    num_conv_pos_embedding_groups
        __a       =    len(self.conv_dim						)
        __a       =    num_hidden_layers
        __a       =    intermediate_size
        __a       =    squeeze_factor
        __a       =    max_position_embeddings
        __a       =    position_buckets
        __a       =    share_att_key
        __a       =    relative_attention
        __a       =    norm_rel_ebd
        __a       =    list(lowerCamelCase						)
        __a       =    hidden_act
        __a       =    num_attention_heads
        __a       =    hidden_dropout
        __a       =    attention_dropout
        __a       =    activation_dropout
        __a       =    feat_proj_dropout
        __a       =    final_dropout
        __a       =    layer_norm_eps
        __a       =    feature_layer_norm_eps
        __a       =    initializer_range
        __a       =    vocab_size

        if (
            (len(self.conv_stride						) != self.num_feat_extract_layers)
            or (len(self.conv_kernel						) != self.num_feat_extract_layers)
            or (len(self.conv_dim						) != self.num_feat_extract_layers)
        ):
               raise ValueError(
                   "Configuration for convolutional layers is incorrect."
                   "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
                   F"but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)"
                   F"= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`."						)

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __a       =    apply_spec_augment
        __a       =    mask_time_prob
        __a       =    mask_time_length
        __a       =    mask_time_min_masks
        __a       =    mask_feature_prob
        __a       =    mask_feature_length
        __a       =    mask_feature_min_masks

        # ctc loss
        __a       =    ctc_loss_reduction
        __a       =    ctc_zero_infinity

        # sequence classification
        __a       =    use_weighted_layer_sum
        __a       =    classifier_proj_size






 @property
 def 						a__   ( self						):
        return functools.reduce(operator.mul    ,		self.conv_stride    ,		1						)
